Evolving Reaction-Diffusion Systems on GPU

@InProceedings{Yamamoto:2011:EPIA,
author = "Lidia Yamamoto and Wolfgang Banzhaf and
Pierre Collet",
title = "Evolving Reaction-Diffusion Systems on {GPU}",
booktitle = "Proceedings 15th Portuguese Conference on Artificial
Intelligence, {EPIA 2011}",
year = "2011",
editor = "Luis Antunes and Helena Sofia Pinto",
volume = "7026",
series = "Lecture Notes in Computer Science",
pages = "208–-223",
address = "Lisbon, Portugal",
month = oct # " 10-13",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, GPU",
isbn13 = "978-3-642-24768-2",
DOI = "doi:10.1007/978-3-642-24769-9_16",
size = "16 pages",
abstract = "Reaction-diffusion systems contribute to various
morphogenetic processes, and can also be used as
computation models in real and artificial chemistries.
Evolving reaction-diffusion solutions automatically is
interesting because it is otherwise difficult to
engineer them to achieve a target pattern or to perform
a desired task. However most of the existing work
focuses on the optimization of parameters of a fixed
reaction network. In this paper we extend this state of
the art by also exploring the space of alternative
reaction networks, with the help of GPU hardware. We
compare parameter optimization and reaction network
optimization on the evolution of reaction-diffusion
solutions leading to simple spot patterns. Our results
indicate that these two optimization modes tend to
exhibit qualitatively different evolutionary dynamics:
in the former, the fitness tends to improve
continuously in gentle slopes, while the latter tends
to exhibit large periods of stagnation followed by
sudden jumps, a sign of punctuated equilibria.",
notes = "Says GP analogue",
}